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English(EN) Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots

新方法从时间快照中识别SDE参数

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的方法，仅使用数据的时域快照来识别随机微分方程（SDE）的漂移和扩散参数。这种名为APPEX（Alternating Projection Parameter Estimation from X0）的方法，在无法观测到单个轨迹时特别有用，例如在单细胞生物学数据集中。该方法还可以恢复SDE的因果图，为跨多个领域的动态系统提供了新的见解。 AI

影响 这项研究为理解动态系统提供了一个新的分析工具，可能影响生成模型和生物数据分析等领域的AI应用。

排序理由 该集群包含一篇详细介绍分析随机微分方程新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新方法从时间快照中识别SDE参数

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该集群包含一篇详细介绍分析随机微分方程新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Vincent Guan, Joseph Janssen, Hossein Rahmani, Andrew Warren, Stephen Zhang, Elina Robeva, Geoffrey Schiebinger ·

    从时间快照中识别漂移、扩散和因果结构

    arXiv:2410.22729v5 Announce Type: replace Abstract: Stochastic differential equations (SDEs) are a fundamental tool for modelling dynamic processes, including gene regulatory networks (GRNs), contaminant transport, financial markets, and image generation. However, learning the un…

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